Radiology leaders have gathered for over 100 years running at RSNA, the annual meeting of the Radiological Society of North America, to discuss the industry’s latest challenges and opportunities. In recent years, AI in medical imaging has become a key focus — with startups at the center of the conversation.
Startups around the world are building AI solutions for a universal problem in medical imaging: limited time. Faced with rising numbers of patients being imaged, as well as the growing size of MRI and CT scans, radiologists must interpret one image every three or four seconds to keep up with the workload.
Agile startups are well-suited to tackle the demands of a rapidly evolving field like deep learning. In medical imaging, many are using AI to develop applications that target areas that slow radiologists down.
Healthcare startups raised more than $26 billion in venture capital funding last year and are partnering with major research institutions, hospitals and medical instrument manufacturers. They’re also receiving regulatory validation for clinical use: over three dozen healthcare AI startups have FDA clearance for algorithms that detect conditions including cancer, stroke and brain hemorrhages from medical scans.
At RSNA 2019, taking place in Chicago, Dec. 1-6, more than 50 attending startups are part of the NVIDIA Inception virtual accelerator program, which provides AI training and tools to fuel the growth of thousands of companies building GPU-powered applications, including over 700 healthcare startups.
Scan the Show for NVIDIA Inception Startups
Accelerated by NVIDIA GPUs, AI can speed up the acquisition, annotation and analysis of medical images to more quickly spot critical cases. It can also give experts quantitative insights that are too time-consuming to acquire using traditional methods.
Dozens of Inception companies will share their medical imaging applications for every phase of the radiology workflow at the RSNA AI Theater and the NVIDIA booth, including:
- Higher-quality scans: Subtle Medical has developed the first and only AI software solutions FDA-cleared for medical imaging enhancement — SubtlePET for faster PET exams and SubtleMR for higher-quality MRI exams. Its software smoothly integrates with any scanner to enhance images during acquisition without altering the existing workflow, increasing efficiency and patient comfort. The company uses the NVIDIA DGX Station and NVIDIA DGX-1 to accelerate training, and NVIDIA T4 GPUs for inference.
- Enabling AI-assisted annotation: TrainingData.io’s web platform helps researchers and companies manage their data labeling workflows, running on NVIDIA T4 GPUs for inference in Google Cloud. The startup leverages AI-assisted segmentation tools through the NVIDIA Clara Train SDK to label medical images that in turn train deep learning models for radiologists. And Palo Alto-based Fovia Ai, Inc. provides its customers with AI-assisted annotation powered by the NVIDIA Clara SDK in its tools for 2D and 3D visualization of medical images, which can seamlessly integrate into the clinical workflow.
- Analyzing medical images: Tokyo startup LPIXEL develops deep learning image analysis tools using NVIDIA GPUs, including one to identify brain aneurysms from MRA, recently approved for clinical use in Japan. For lung tumor detection, China-based InferVISION’s AI tools identify and label lung nodules from CT scans in under 30 seconds. The company uses NVIDIA T4 GPUs for inference, achieving speedups of 4x over CPUs.
- Processing surgical video: Doctors performing minimally invasive surgeries rely on live video feeds from tiny cameras to view the area they’re operating on. Kaliber Labs is building deep learning models that interpret these video feeds in real time for orthopedic surgery, identifying and measuring aspects of the patient’s anatomy and pathology, and providing intraoperative guidance to surgeons. The startup is using NVIDIA RTX GPUs for training and the NVIDIA Jetson AGX Xavier AI computing module for inference at the edge.
Rounding Out RSNA
In NVIDIA booth 10939 and beyond, we’ll be exhibiting the latest AI tools for medical imaging, from training to deployment.
We’ll also showcase demos of the NVIDIA Clara medical imaging platform, which combines NVIDIA GPU hardware and the NVIDIA Clara software development kit to accelerate the training and inference of deep learning applications for healthcare. The platform includes APIs for AI-assisted annotation of medical images, a transfer learning toolkit, a medical model development environment and tools for AI deployment at scale.
A Clara developer meetup will be held on Tuesday, Dec. 3 at 11:30 a.m. CT.
The following RSNA panels feature NVIDIA speakers:
- Integrating the Healthcare Enterprise on Fast Healthcare Interoperability Resources — Monday, Dec. 2, at 8:30 a.m. CT
- Deep Learning: How to Get Started — Thursday, Dec. 5, at 8:30 a.m. CT
- Commercial Development and Deployment of Deep Learning Technology — Thursday, Dec. 5, at 4:30 p.m. CT
For more information, check out the full RSNA agenda.